Automatic license plate recognition (ALPR) technology as a means for vehicle identification has broad applications such as tolling, parking management, traffic violation enforcement, etc. A specific application of interest involves identifying a target vehicle from a partially known license plate number. Such approaches, however, have problems. Existing ALPR technology, for example, is far from perfect. In some instances, U can be mistaken as V, Z can be mistaken as 2, ALPR may drop or merge characters due to non-ideal sensing, and/or character segmentation, etc.
Consequently, in law enforcement situations such as an Amber Alert, it makes sense to err on the conservative side and release an alert when there is still some uncertainty about the wanted plate number. There is a large degree of uncertainty regarding how conservative the license plate matching system can function. Fortunately, common ALPR mistakes are not completely random. There are some dependencies in the appearance of the plate numbers. There are also some dependencies between ALPR mistakes and the sensing/object conditions. Thus, there exists a need to exploit these dependencies and provide an efficient and effective license plate matching system for use in high priority situations such as Amber Alerts.